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Prediction accuracy of bivariate score-driven risk premium and volatility filters: an illustration for the Dow Jones

Adrian Licht, Alvaro Escribano () and Szabolcs Blazsek ()

UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía

Abstract: In this paper, we introduce Beta-t-QVAR (quasi-vector autoregression) for the joint modelling of score-driven location and scale. Asymptotic theory of the maximum likelihood (ML) estimatoris presented, and sufficient conditions of consistency and asymptotic normality of ML are proven. Forthe joint score-driven modelling of risk premium and volatility, Dow Jones Industrial Average (DJIA)data are used in an empirical illustration. Prediction accuracy of Beta-t-QVAR is superior to theprediction accuracies of Beta-t-EGARCH (exponential generalized AR conditional heteroscedasticity),A-PARCH (asymmetric power ARCH), and GARCH (generalized ARCH). The empirical results motivate the use of Beta-t-QVAR for the valuation of DJIA options.

Keywords: Generalized; Autoregressive; Score; Dynamic; Conditional; Score; Risk; Premium; Volatility (search for similar items in EconPapers)
JEL-codes: C22 C58 (search for similar items in EconPapers)
Date: 2020-11-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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